TY - JOUR
T1 - Metabolite profiling of CKD progression in the chronic renal insufficiency cohort study
AU - Wen, Donghai
AU - Zheng, Zihe
AU - Surapaneni, Aditya
AU - Yu, Bing
AU - Zhou, Linda
AU - Zhou, Wen
AU - Xie, Dawei
AU - Shou, Haochang
AU - Avila-Pacheco, Julian
AU - Kalim, Sahir
AU - He, Jiang
AU - Hsu, Chi Yuan
AU - Parsa, Afshin
AU - Rao, Panduranga
AU - Sondheimer, James
AU - Townsend, Raymond
AU - Waikar, Sushrut S.
AU - Rebholz, Casey M.
AU - Denburg, Michelle R.
AU - Kimmel, Paul L.
AU - Vasan, Ramachandran S.
AU - Clish, Clary B.
AU - Coresh, Josef
AU - Feldman, Harold I.
AU - Grams, Morgan E.
AU - Rhee, Eugene P.
N1 - Funding Information:
FUNDING. This study was supported by the NIH (U01 DK106981, U01 DK106982, U01 DK085689, R01 DK108803, and R01 DK124399).
Funding Information:
This study was supported by the CKD Biomarkers Consortium, including U01 DK106981 (principal investigator [PI]: EPR), U01 DK106982 (PI: MRD), and U01 DK085689 (PI: JC). Additional funding was from R01 DK108803 and R01 DK124399 (PI: MEG). Funding for the CRIC Study was obtained under a cooperative agreement from NIDDK (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, U01DK060902, and U24DK060990). In addition, this work was supported in part by: the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award NIH/NCATS UL1TR000003, Johns Hopkins University UL1 TR000424, University of Maryland GCRC M01 RR16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the NIH and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/NCRR UCSF-CTSI UL1 RR024131, Department of Internal Medicine, and University of New Mexico School of Medicine Albuquerque, NM R01DK119199. The AASK was conducted by the AASK Investigators and supported by the NIDDK. This manuscript was not prepared in collaboration with investigators of the AASK study and does not necessarily reflect the opinions or views of the AASK study, the NIH, or the NIDDK. The AASK trial and cohort were supported by institutional grants from the NIH and NIDDK (M01 RR00080, M01 RR00071, M0100032, P20 RR11145, M01 RR00827, M01 RR00052, 2P20 RR11104, RR029887, DK 281802, DK057867, and DK048689), and the following pharmaceutical companies (King Pharmaceuticals, Pfizer, AstraZene-ca, GlaxoSmithKline, Forest Laboratories, Pharmacia, and Upjohn). The ARIC study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, NIH, Department of Health and Human Services, under Contract nos. 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005. The authors thank the staff and participants of the ARIC study for their important contributions. Some of the data reported here have been supplied by the USRDS. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government. The opinions presented in this paper do not necessarily reflect those of the NIDDK, the NIH, the Department of Health and Human Services, or the government of the United States.
Publisher Copyright:
© 2022, Wen et al. This is.
PY - 2022/10/24
Y1 - 2022/10/24
N2 - BACKGROUND. Metabolomic profiling in individuals with chronic kidney disease (CKD) has the potential to identify novel biomarkers and provide insight into disease pathogenesis. METHODS. We examined the association between blood metabolites and CKD progression, defined as the subsequent development of end-stage renal disease (ESRD) or estimated glomerular filtrate rate (eGFR) halving, in 1,773 participants of the Chronic Renal Insufficiency Cohort (CRIC) study, 962 participants of the African-American Study of Kidney Disease and Hypertension (AASK), and 5,305 participants of the Atherosclerosis Risk in Communities (ARIC) study. RESULTS. In CRIC, more than half of the measured metabolites were associated with CKD progression in minimally adjusted Cox proportional hazards models, but the number and strength of associations were markedly attenuated by serial adjustment for covariates, particularly eGFR. Ten metabolites were significantly associated with CKD progression in fully adjusted models in CRIC; 3 of these metabolites were also significant in fully adjusted models in AASK and ARIC, highlighting potential markers of glomerular filtration (pseudouridine), histamine metabolism (methylimidazoleacetate), and azotemia (homocitrulline). Our findings also highlight N-acetylserine as a potential marker of kidney tubular function, with significant associations with CKD progression observed in CRIC and ARIC. CONCLUSION. Our findings demonstrate the application of metabolomics to identify potential biomarkers and causal pathways in CKD progression.
AB - BACKGROUND. Metabolomic profiling in individuals with chronic kidney disease (CKD) has the potential to identify novel biomarkers and provide insight into disease pathogenesis. METHODS. We examined the association between blood metabolites and CKD progression, defined as the subsequent development of end-stage renal disease (ESRD) or estimated glomerular filtrate rate (eGFR) halving, in 1,773 participants of the Chronic Renal Insufficiency Cohort (CRIC) study, 962 participants of the African-American Study of Kidney Disease and Hypertension (AASK), and 5,305 participants of the Atherosclerosis Risk in Communities (ARIC) study. RESULTS. In CRIC, more than half of the measured metabolites were associated with CKD progression in minimally adjusted Cox proportional hazards models, but the number and strength of associations were markedly attenuated by serial adjustment for covariates, particularly eGFR. Ten metabolites were significantly associated with CKD progression in fully adjusted models in CRIC; 3 of these metabolites were also significant in fully adjusted models in AASK and ARIC, highlighting potential markers of glomerular filtration (pseudouridine), histamine metabolism (methylimidazoleacetate), and azotemia (homocitrulline). Our findings also highlight N-acetylserine as a potential marker of kidney tubular function, with significant associations with CKD progression observed in CRIC and ARIC. CONCLUSION. Our findings demonstrate the application of metabolomics to identify potential biomarkers and causal pathways in CKD progression.
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U2 - 10.1172/jci.insight.161696
DO - 10.1172/jci.insight.161696
M3 - Article
C2 - 36048534
AN - SCOPUS:85140417157
SN - 2379-3708
VL - 7
JO - JCI insight
JF - JCI insight
IS - 20
M1 - e161696
ER -